  =============================================
   two (fake) Swiss towns                      
  =============================================
  MIGRATION RATE AND POPULATION SIZE ESTIMATION
  using Markov Chain Monte Carlo simulation
  =============================================
  Version debug 4.2.7

  Program started at Fri Apr  1 18:59:48 2016
         finished at Fri Apr  1 19:27:28 2016
     


Options in use:
---------------

Analysis strategy is BAYESIAN INFERENCE

Proposal distribution:
Parameter group          Proposal type
-----------------------  -------------------
Population size (Theta)  Metropolis sampling
Migration rate      (M)  Metropolis sampling


Prior distribution (Proposal-delta will be tuned to acceptance frequence 0.440000):
Parameter group            Prior type   Minimum    Mean(*)    Maximum    Delta
-------------------------  ------------ ---------- ---------- ---------- ----------
Population size (Theta_1)      Uniform  0.000000   0.050000   0.100000   0.010000 
Population size (Theta_2)      Uniform  0.000000   0.050000   0.100000   0.010000 
Migration 1 to 2 (M)      Uniform  0.000000  500.000000 1000.00000 100.000000




Inheritance scalers in use for Thetas (specified scalars=1)
1.00 1.00 1.00 
[Each Theta uses the (true) inheritance scalar of the first locus as a reference]


Pseudo-random number generator: Mersenne-Twister                                
Random number seed (with internal timer)           3966064065

Start parameters:
   First genealogy was started using a random tree
   Start parameter values were generated
Connection matrix:
m = average (average over a group of Thetas or M,
s = symmetric migration M, S = symmetric 4Nm,
0 = zero, and not estimated,
* = migration free to vary, Thetas are on diagonal
d = row population split off column population
D = split and then migration
   1 Aadorf         * c 
   2 Bern           * * 



Mutation rate is constant for all loci

Markov chain settings:
   Long chains (long-chains):                              1
      Steps sampled (inc*samples*rep):               1000000
      Steps recorded (sample*rep):                     20000
   Combining over replicates:                              4
   Static heating scheme
      4 chains with  temperatures
       1.00, 1.50, 3.00,1000000.00
      Swapping interval is 1
   Burn-in per replicate (samples*inc):               250000

Print options:
   Data file:                                  twoswisstowns
   Haplotyping is turned on:                              NO
   Output file (ASCII text):         outfile-twoswisstowns_c
   Output file (PDF):            outfile-twoswisstowns_c.pdf
   Posterior distribution:                         bayesfile
   Print data:                                            No
   Print genealogies:                                     No

Summary of data:
Title:                                two (fake) Swiss towns
Data file:                                     twoswisstowns
Datatype:                                     Haplotype data
Number of loci:                                            3
Mutationmodel:
 Locus  Sublocus  Mutationmodel   Mutationmodel parameter
-----------------------------------------------------------------
     1         1 Felsenstein 84  [Bf:0.26 0.27 0.22 0.25, t/t ratio=2.000]
     1         2 Felsenstein 84  [Bf:0.24 0.27 0.22 0.27, t/t ratio=2.000]
     2         1 Felsenstein 84  [Bf:0.26 0.23 0.25 0.25, t/t ratio=2.000]
     3         1 Felsenstein 84  [Bf:0.26 0.24 0.24 0.26, t/t ratio=2.000]


Sites per locus
---------------
Locus    Sites
     1     500 500
     2     300
     3     700

Population                   Locus   Gene copies    
----------------------------------------------------
  1 Aadorf                       1        10
  1 Aadorf                       1        10
  1                              2        10
  1                              3        10
  2 Bern                         1        10
  2 Bern                         1        10
  2                              2        10
  2                              3        10
    Total of all populations     1        40
                                 2        20
                                 3        20




Bayesian estimates
==================

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         0.00253  0.01040  0.01697  0.02440  0.02800  0.01690  0.01701
    1  Theta_2         0.00233  0.00507  0.00897  0.01447  0.04567  0.01797  0.02847
    1  M_1->2          48.0000  58.0000 101.6667 262.6667 475.3333 227.0000 397.9196
    2  Theta_1         0.00787  0.01813  0.02037  0.02300  0.04467  0.02343  0.02516
    2  Theta_2         0.00493  0.03273  0.03890  0.04827  0.04973  0.02850  0.05291
    2  M_1->2         200.0000 384.0000 475.0000 488.6667 510.0000 385.6667 641.6697
    3  Theta_1         0.00533  0.01513  0.01643  0.01760  0.03813  0.01763  0.01868
    3  Theta_2         0.01513  0.03400  0.04603  0.04860  0.05073  0.03557  0.06214
    3  M_1->2         150.6667 290.6667 355.6667 450.0000 502.6667 337.0000 492.2996
  All  Theta_1         0.01153  0.01713  0.01750  0.01800  0.02440  0.01797  0.01816
  All  Theta_2         0.01413  0.02407  0.03463  0.04007  0.04907  0.02797  0.04185
  All  M_1->2         163.3333 380.0000 439.0000 480.0000 509.3333 360.3333 476.0137
-----------------------------------------------------------------------------------



Log-Probability of the data given the model (marginal likelihood = log(P(D|thisModel))
--------------------------------------------------------------------
[Use this value for Bayes factor calculations:
BF = Exp[log(P(D|thisModel) - log(P(D|otherModel)]
shows the support for thisModel]



Locus      Raw Thermodynamic score(1a)  Bezier approximated score(1b)      Harmonic mean(2)
------------------------------------------------------------------------------------------
      1              -2544.92                      -2281.12               -2252.08
      2               -865.00                       -769.59                -750.26
      3              -2009.44                      -1760.44               -1730.18
---------------------------------------------------------------------------------------
  All                -5402.28                      -4794.07               -4715.43
[Scaling factor = 17.080671]


MCMC run characteristics
========================




Acceptance ratios for all parameters and the genealogies
---------------------------------------------------------------------

Parameter           Accepted changes               Ratio
Theta_1                 175900/416373            0.42246
Theta_2                 294115/416713            0.70580
M_1->2                  254578/415512            0.61269
Genealogies             139544/1751402            0.07968

Autocorrelation and Effective sample size
-------------------------------------------------------------------

  Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.49491             20860.47
  Theta_2                0.22861             38308.12
  M_1->2                 0.43962             23961.82
  Ln[Prob(D|P)]          0.65145             12677.71
  (*) averaged over loci.





Assignment of Individuals to populations
========================================

Individual Locus        1     2 
---------- -------- ----- ----- 
?BAG               1 0.163 0.837 
?BAG               2 0.741 0.259 
?BAG               3 0.882 0.118 
?BAG             All 0.806 0.194 
?BAJ               1 0.278 0.722 
?BAJ               2 0.530 0.470 
?BAJ               3 0.000 1.000 
?BAJ             All 0.000 1.000 
?BAH               1 0.115 0.885 
?BAH               2 0.741 0.259 
?BAH               3 0.000 1.000 
?BAH             All 0.000 1.000 
?BAI               1 0.447 0.553 
?BAI               2 0.470 0.530 
?BAI               3 0.278 0.722 
?BAI             All 0.216 0.784 
?BAF               1 0.610 0.390 
?BAF               2 0.741 0.259 
?BAF               3 0.118 0.882 
?BAF             All 0.375 0.625 
